RAG AI Chatbot & Knowledge Base

About this Gig

Build a production-ready Retrieval-Augmented Generation (RAG) AI chatbot that delivers accurate, context-aware answers using your organization's knowledge instead of relying solely on an LLM's training data. A RAG system enables AI to search, retrieve, and understand information from your documents before generating responses, significantly reducing hallucinations while improving accuracy and reliability. I develop custom RAG solutions that connect AI models with PDFs, Word documents, websites, databases, FAQs, manuals, internal documentation, and other business knowledge sources. Whether you need an internal knowledge assistant, customer support chatbot, employee helpdesk, documentation assistant, or AI-powered search system, I can build a solution tailored to your business. Services include: • Custom RAG chatbot development • AI-powered document question answering • PDF, DOCX, and text document ingestion • Knowledge base creation and management • Vector database integration • Embedding pipeline implementation • Semantic search and document retrieval • FastAPI backend development • REST API development • Multi-document support • Website knowledge extraction • Database integration • Dockerized deployment • Cloud deployment support • Authentication-ready architecture Ideal use cases: ✓ Company knowledge assistants ✓ Customer support chatbots ✓ Internal employee assistants ✓ HR policy assistants ✓ Legal document search ✓ Medical knowledge assistants ✓ Product documentation chatbots ✓ Technical support assistants ✓ Educational AI tutors ✓ AI-powered enterprise search Core Features: ✓ Retrieval-Augmented Generation (RAG) ✓ Semantic search ✓ Document chunking ✓ Embeddings ✓ Vector database integration ✓ Source-aware responses ✓ Multi-document support ✓ FastAPI backend ✓ REST APIs ✓ Streaming AI responses ✓ Scalable architecture ✓ Docker support ✓ Cloud deployment ✓ Production-ready implementation Technology Stack: Python • FastAPI • Agno • PostgreSQL • PgVector • SQLite • Vector Databases • Embeddings • Gemini • OpenAI-Compatible Models • Hugging Face • REST APIs • Docker • Railway • Render • Vercel Development Process: 1. Understand your business and knowledge sources 2. Design the RAG architecture 3. Process and index your documents 4. Configure embeddings and vector search 5. Integrate the LLM and retrieval pipeline 6. Build the chatbot and backend APIs 7. Test retrieval quality and response accuracy 8. Deploy and document the complete solution Every business has different knowledge sources and retrieval requirements. I build scalable RAG systems optimized for accuracy, maintainability, performance, and real-world production use.

Requirements

To build the most effective RAG solution, please provide the following information: • A brief description of your business, project, or application • The primary purpose of the chatbot and the type of users it will serve • The documents or knowledge sources the AI should use, such as PDFs, DOCX files, websites, FAQs, manuals, policies, technical documentation, or databases • Approximately how many documents or pages need to be indexed • Whether the knowledge base will remain static or require regular updates • Any APIs, databases, CRMs, or third-party services that need to be integrated • Your preferred AI model or provider (OpenAI, Gemini, open-source models, etc.), if applicable • Whether authentication, user-specific access, or permissions are required • Your preferred deployment environment (Docker, Railway, Render, cloud server, or existing infrastructure) • Existing codebase, repositories, API documentation, or architecture diagrams (if available) • Project timeline, milestones, and any technical or business requirements If you're unsure about embeddings, vector databases, chunking strategies, retrieval methods, or system architecture, simply share your documents and describe your desired outcome. I'll recommend the most suitable RAG architecture for your use case. For security reasons, please do not share production API keys, passwords, or sensitive credentials in your initial message.

Related Tags